Mechanism Based Toxicity database

Another typical source of uncertainty in mixture assessment is the potential interaction between substances. Interactions may occur in the environment (e.g., precipitation after emission in water), during absorption, transportation, and transformation in the organism, or at the site of toxic action. Interactions can be either direct, for example, a chemical reaction between 2 or more mixture components, or indirect, for example, if 1 mixture component blocks an enzyme that metabolizes another mixture component (see Chapters 1 and 2). Direct interactions between mixture components are relatively easy to predict based on physical-chemical data, but prediction of indirect interactions is much more difficult because it requires detailed information about the processes involved in the toxic mechanisms of action. One of the main challenges in mixture risk assessment is the development of a method to predict mixture interactions. A first step toward such a method could be the setup of a database, which contains the results of mixture toxicity tests. Provided such a database would contain sufficient data, it could be used to predict the likelihood and magnitude of potential interaction effects, that is, deviations for CA and RA. This information could subsequently be used to decide whether application of an extra safety factor for potential interaction effects is warranted, and to determine the size of such a factor. The mixture toxicity database could also support the search for predictive parameters of interaction effects, for example, determine which modes of action are involved in typical interactions. 

In silico tools make a significant contribution to the SAR-based early identification of potential toxicity. An increasing volume of published preclinical and clinical toxicity data are collected and used to build structure-related searchable databases. These expert knowledge databases can analyze chemical structures and match them with potential mechanisms of toxicity. DEREK for Windows (Lhasa Ltd.)39 is one of such broadly used knowledge-based expert systems to provide toxicology alerts for new compounds. Although certainly not comprehensive, numerous efforts have been made to predict hepatotoxicity. Recently,... 

There is enormous variability in the extent and nature of different databases for risk assessment. For example, in some cases, the evaluation must be based on limited data in experimental animals in other cases detailed information on the mechanism of toxicity and/or toxicokinetics may be available. In some cases the risk evaluation can be based on effects data in exposed human populations however, few chemicals have been adequately studied in humans to accurately identify a safe dose directly. Therefore, scientists typically rely on existing human... 

The Food Quality Protection Act of 1996 mandated USA EPA to "upgrade its risk assessment process as part of the tolerance setting procedures" (3), The changes to risk assessment were based in part on recommendations from the National Academy of Sciences report (22), The act required an explicit determination that tolerances were safe to children. US EPA was required to use an extra 10-fold safety factor to take into account both pre-/post natal developmental toxicity and the completeness of the database, unless US EPA determined, based on reliable data, that a different margin would be safe. In addition, US EPA must consider available information on 1/ aggregate exposure from all non-occupational sources 2/ effects of cumulative exposure to the pesticide plus others with a common mechanism of toxicity 3/ effects of in utero exposure 4/ the potential for endocrine disrupting effects. 

The software now uses structurally intrinsic parameters for only one QSAR model (LSER) and the results are used to predict one property (acute toxicity) to four aquatic species by one mechanism (nonreactive, non-polar narcosis) however, we intend to continue to refine our equations as databases grow, incorporate other models, predict other properties, and include other organisms. We will attempt to differentiate between modes of toxic action and improve our estimates accordingly. For the widely divergent classes of chemicals and types of environmental behavior, no one model will best describe every situation and no one species is the optimal organism to monitor. As the software evolves, the expert system should choose the best model based on the contaminant, the species, and the property to be predicted (e.g., toxicity or bioaccumulation). In addition, we envision an interactive screen system for data entry that will bypass the SMILES notation and allow the user to describe the molecule by posing a series of questions about the compound s backbone and functional groups. The responses will translate directly into values of LSER variables. 

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